The present disclosure relates to an image processing technology and, more particularly, to an image processor for tracking a target by analyzing a movie, and an image processing method carried out therein.
Visual tracking holds promise for application to a variety of sectors including computer vision and, more particularly, visual monitoring in the security field, analysis, classification, and editing of documentary films in the audiovisual field, or man-machine and human-to-human interfaces, i.e., television (TV) conference and TV phone. Therefore, many studies have been made to ensure improved tracking accuracy and processing efficiency. Above all, a number of studies have been conducted to apply a particle filter to visual tracking. Particle filter has attracted attention as a chronological analysis technique for signals added with non-Gaussian noise. It is difficult to deal with non-Gaussian noise with a Kalman filter. In particular, the Condensation (Conditional Density Propagation) algorithm is famous (refer, for example, to Contour tracking by stochastic propagation of conditional density, Michael Isard and Andrew Blake, Proc. European Conf. on Computer Vision, vol. 1, pp. 343-356, Cambridge UK (1996) (hereinafter referred to as Non-Patent Document 1) and ICondensation: Unifying low-level and high-level tracking in a stochastic framework, Michael Isard and Andrew Blake, Proc. 5th European Conf. Computer Vision, 1998 (hereinafter referred to as Non-Patent Document 2)).
Particle filter is an approximation technique which represents a target probability distribution by introducing a finite number of particles as candidates for tracking and then performs a time-series estimation and prediction. When a particle filter is used for visual tracking, the motion of a parameterized target is treated as a single particle so that a presence distribution probability is estimated successively in a parametric space of interest by moving particles using a kinetic model and calculating the likelihood of the movement result through observation.